Currently, I am working on a program that integrates x + x^2 + e^x + 2cos(x/2) - 1 with three input variables, a, b, and n. What I need returned is the numerical integral from a to b with n increments. The function also has to return trapezoids for each n as a column vector. Thus, the integral value as a scalar, and a vector of values.
I've gotten to a point where the function int_f_1 is undefined for some reason, and I have no idea why. I thought by nesting that function under the test function, it would help. But it does not, and I don't know why that is. Any suggestions?
function [y] = test_function_1(x);
y = x + x.^2 + exp(x) + 2*cos(x/2) - 1
end
function [int_f, increment] = int_f_1 (a, b, n);
f = #test_function_1;
h = a + b ./ n
increments = h
int_f = integral(h, f)
end
In Unison, functions are identified by the hashes of their ASTs instead of by their names.
Their documentation and their FAQs have given some explanations of the mechanism.
However, the example presented in the link is not clear to me how the hashing actually works:
They used an example
f x = g (x - 1)
g x = f (x / 2)
which in the first step of their hashing is converted to the following:
$0 =
f x = $0 (x - 1)
g x = $0 (x / 2)
Doesn't this lose information about the definitions.
For the two following recursively-defined functions, how can the hashing distinguish them:
# definition 1
f x = g (x / 2)
g x = h (x + 1)
h x = f (x * 2 - 7)
# definition 2
f x = h (x / 2)
g x = f (x + 1)
h x = g (x * 2 - 7)
In my understanding, brutally converting all calling of f g and h to $0 would make the two definitions undistinguishable from each other. What am I missing?
The answer is that the form in the example (with $0) is not quite accurate. But in short, there's a special kind of hash (a "cycle hash") which is has the form #h.n where h is the hash of all the mutually recursive definitions taken together, and n is a number from 0 to the number of terms in the cycle. Each definition in the cycle gets the same hash, plus an index.
The long answer:
Upon seeing cyclical definitions, Unison captures them in a binding form called Cycle. It's a bit like a lambda, but introduces one bound variable for each definition in the cycle. References within the cycle are then replaced with those variables. So:
f x = g (x - 1)
g x = f (x / 2)
Internally becomes more like (this is not valid Unison syntax):
$0 = Cycle f g ->
letrec
[ x -> g (x - 1)
, x -> f (x / 2) ]
It then hashes each of the lambdas inside the letrec and sorts them by that hash to get a canonical order. Then the whole cycle is hashed. Then these "cycle hashes" of the form #h.n get introduced at the top level for each lambda (where h is the hash of the whole cycle and n is the canonical index of each term), and the bound variables get replaced with the cycle hashes:
#h.0 = x -> #h.1 (x - 1)
#h.1 = x -> #h.0 (x / 2)
f = #h.0
g = #h.1
We define the sigmoidal function
σ(t) = 1 / (1+e−t)
It has the derivative σ′(t) = σ(t)(1 − σ(t)). The module gauss_newton contains a function generate_data(gamma=0) which generates a data set (ti , αi ) where ti ∈ R and αi ∈ R with
αi = σ(6ti + 1) + εiγ.
for i = 1, . . . , 10. The values εi ∼ N (0, 1) are independently normally distributed and the real value γ ∈ R controls the influence of εi.
(i) Solve the problem min (1/2(∥F(x)∥^2),
with Fi(x) = σ(x1ti + x2) − αi for i = 1,...,10 and γ = 0 using the Gauss Newton algorithm . Iterate until the size of the search direction is sufficiently small, i.e. until ∥∆xk ∥ < δ for some tolerance δ > 0.
I have an implicit equation, like this:
(a1 X + b1 Y + m)*(a2 X + b2 Y + m)*(a3 X + b3 Y + m) - c = 0,
a1,a2,b1,b2,a3,b3 are certain value, c is a variant. According different c, I need to solve it to get a set of (x,y), which I will use to integrate.
The listed function I have in practice is much more complex than this, so I am confused as to why when I put this equation into the website desmos to draw this implicit function, I can get the solutions that satisfies this function and I would like to know why this is so fast for desmos and then if there is a better way to find these solutions
I using polar coordinate to solve this problem
c_range = np.linspace(0,c_max, 1000)
theta_range = np.linspace(0,pi, 1000)
for i in range(1000):
if i ==0:
c = c_range[i]
for j in range(1000):
theta = theta_range[j]
r = fsolve(#func, r0, args=(c, theta))
radius[i][j] = r
r0 = r
else:
c = c_range[i]
r0 = radius[i-1]
r = fsolve(#func, r0, args=(c, theta_range))
radius[i]= r
I wrote this mips code to find the gcf but I am confused on getting the number of instructions executed for this code. I need to find a linear function as a function of number of times the remainder must be calculated before an answer. i tried running this code using Single step with Qtspim but not sure on how to proceed.
gcf:
addiu $sp,$sp,-4 # adjust the stack for an item
sw $ra,0($sp) # save return address
rem $t4,$a0,$a1 # r = a % b
beq $t4,$zero,L1 # if(r==0) go to L1
add $a0,$zero,$a1 # a = b
add $a1,$zero,$t4 # b = r
jr gcf
L1:
add $v0,$zero,$a1 # return b
addiu $sp,$sp,4 # pop 2 items
jr $ra # return to caller
There is absolutely nothing new to show here, the algorithm you just implemented is the Euclidean algorithm and it is well known in the literature1.
I will nonetheless write an informal analysis here as link only questions are evil.
First lets rewrite the code in an high level formulation:
unsigned int gcd(unsigned int a, unsigned int b)
{
if (a % b == 0)
return b;
return gcd(b, a % b);
}
The choice of unsigned int vs int was dicated by the MIPS ISA that makes rem undefined for negative operands.
Out goal is to find a function T(a, b) that gives the number of step the algorithm requires to compute the GDC of a and b.
Since a direct approach leads to nothing, we try by inverting the problem.
What pairs (a, b) makes T(a, b) = 1, in other words what pairs make gcd(a, b) terminates in one step?
We clearly must have that a % b = 0, which means that a must be a multiple of b.
There are actually an (countable) infinite number of pairs, we can limit our selves to pairs with the smallest, a and b2.
To recap, to have T(a, b) = 1 we need a = nb and we pick the pair (a, b) = (1, 1).
Now, given a pair (c, d) that requires N steps, how do we find a new pair (a, b) such that T(a, b) = T(c, d) + 1?
Since gcd(a, b) must take one step further then gcd(c, d) and since starting from gcd(a, b) the next step is gcd(b, a % b) we must have:
c = b => b = c
d = a % b => d = a % c => a = c + d
The step d = a % c => a = c + d comes from the minimality of a, we need the smallest a that when divided by c gives d, so we can take a = c + d since (c + d) % c = c % c d % c = 0 + d = d.
For d % c = d to be true we need that d < c.
Our base pair was (1, 1) which doesn't satisfy this hypothesis, luckily we can take (2, 1) as the base pair (convince your self that T(2, 1) = 1).
Then we have:
gcd(3, 2) = gcd(2, 1) = 1
T(3, 2) = 1 + T(2, 1) = 1 + 1 = 2
gcd(5, 3) = gcd(3, 2) = 1
T(5, 3) = 1 + T(3, 2) = 1 + 2 = 3
gcd(8, 5) = gcd(5, 3) = 1
T(8, 5) = 1 + T(5, 3) = 1 + 3 = 4
...
If we look at the pair (2, 1), (3, 2), (5, 3), (8, 5), ... we see that the n-th pair (starting from 1) is made by the number (Fn+1, Fn).
Where Fn is the n-th Fibonacci number.
We than have:
T(Fn+1, Fn) = n
Regarding Fibonacci number we know that Fn ∝ φn.
We are now going to use all the trickery of asymptotic analysis, particularly in the limit of the big-O notation considering φn or φn + 1 is the same.
Also we won't use the big-O symbol explicitly, we rather assume that each equality is true in the limit. This is an abuse, but makes the analysis more compact.
We can assume without loss of generality that N is an upper bound for both number in the pair and that it is proportional to φn.
We have N ∝ φn that gives logφ N = n, this ca be rewritten as log(N)/log(φ) = n (where logs are in base 10 and log(φ) can be taken to be 1/5).
Thus we finally have 5logN = n or written in reverse order
n = 5 logN
Where n is the number of step taken by gcd(a, b) where 0 < b < a < N.
We can further show that if a = ng and b = mg with n, m coprimes, than T(a, b) = T(n, m) thus the restriction of taking the minimal pairs is not bounding.
1 In the eventuality that you rediscovered such algorithm, I strongly advice against continue with reading this answer. You surely have a sharp mind that would benefit the most from a challenge than from an answer.
2 We'll later see that this won't give rise to a loss of generality.